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. 2006 Jul 1;34(Web Server issue):W498-503.
doi: 10.1093/nar/gkl038.

CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis

Johannes Rainer  1 , Fatima Sanchez-Cabo, Gernot Stocker, Alexander Sturn, Zlatko Trajanoski
Affiliations

CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis

Johannes Rainer et al. Nucleic Acids Res. .

Abstract

CARMAweb (Comprehensive R-based Microarray Analysis web service) is a web application designed for the analysis of microarray data. CARMAweb performs data preprocessing (background correction, quality control and normalization), detection of differentially expressed genes, cluster analysis, dimension reduction and visualization, classification, and Gene Ontology-term analysis. This web application accepts raw data from a variety of imaging software tools for the most widely used microarray platforms: Affymetrix GeneChips, spotted two-color microarrays and Applied Biosystems (ABI) microarrays. R and packages from the Bioconductor project are used as an analytical engine in combination with the R function Sweave, which allows automatic generation of analysis reports. These report files contain all R commands used to perform the analysis and guarantee therefore a maximum transparency and reproducibility for each analysis. The web application is implemented in Java based on the latest J2EE (Java 2 Enterprise Edition) software technology. CARMAweb is freely available at https://carmaweb.genome.tugraz.at.

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Figures

Figure 1
Figure 1
CARMAweb analysis workflow. The different modules of CARMAweb can either be used individually or in combination, resulting in an analytical pipeline. Analysis result files can be returned to the user's data directory and then be used as input for the other modules (e.g. the GO analysis module).
Figure 2
Figure 2
The cluster analysis module GenesisWeb offers interactive cluster selection. (A) Result from a hierarchical cluster analysis. (B) Result from k-means cluster analysis of the same dataset. (C) Result from SOM cluster analysis. (D) Visualization of a CA of the same dataset. Clusters interactively selected in any of the cluster analyses can be highlighted in further analyses (shown here as red labeled genes).
Figure 3
Figure 3
Result workspaces of a differentially expressed genes analysis (left) and a GO analysis (right). (A) Volcano plot displaying the mean differential expression against P-values (−log10 of the P-value) of all genes. (B) MA plot. Points are colored according to local point density with brighter colors coding for higher density. (C) The induced GO graph of the genes of interest. Red nodes represent over-represented GO terms.
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